Device, method, and program for visualizing network of blood vessels of skin

ABSTRACT

A device of an embodiment includes: an optical mechanism that guides light from a light source to skin tissue to scan the skin tissue; a control computation unit that controls driving of the optical mechanism, acquires tomographic images of the skin by processing optical interference signals from the optical system, and calculates a network of blood vessels on the basis of the tomographic images; and a display device that displays the network of blood vessels. The control computation unit computes autocorrelation values at coordinates in epidermis corresponding regions of the tomographic images, excludes combinations of tomographic images with the computed autocorrelation values corresponding to predetermined low autocorrelation, computes autocorrelation values at coordinates in dermis corresponding regions, determines coordinates at which the autocorrelation values in the dermis corresponding regions are within a predetermined low correlation range to be blood vessels or blood vessel candidates, and calculates a network of blood vessels.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of InternationalApplication No. PCT/JP2018/024516, filed on Jun. 28, 2018, which claimspriority to and the benefit of Japanese Patent Application No.2017-189269, filed on Sep. 29, 2017. The contents of these applicationsare incorporated herein by reference in their entirety.

BACKGROUND OF INVENTION 1. Field

The present invention relates to a device and a method for visualizing anetwork of blood vessels of skin by using optical coherence tomography(OCT).

2. Description of Related Art

Skin serves such important roles as preventing loss of moisture,themoregulating through heat exchange with external environment,protecting a living body from physical irritation, and perceiving sensessuch as touch. Skin tissue is mainly constituted by three layers, whichare epidermis, dermis, and subcutaneous tissue. Capillaries run in andunder dermis, and supply oxygen and nutrients to skin cells to providefirmness and moisture to the skin. Reduction in the elasticity ofcapillaries due to aging and environmental changes such as ultravioletrays is considered as a cause of symptoms of aging of the skin such aswinkles and sagging. Thus, technologies of visualizing a network ofblood vessels of skin for evaluation of effective skin care are drawingattention.

A method using the OCT has been proposed as such a technology forvisualizing a network of blood vessels (refer to Non-patent Literature1, for example). The OCT is tomography using low-coherence opticalinterference, which enables visualization of distribution of biologicaltissue forms at high spatial resolution in microscale. The OCT is alsoadvantageous in achieving an image acquisition rate not lower than thevideo rate, and high temporal resolution.

RELATED ART LIST

Non-patent Literature 1: J. Enfield, E. Jonathan, M. Leahy, In vivoimaging of the microcirculation of the volar forearm using correlationmapping optical coherence tomography (cmOCT), Biomed. Opt. Express 2(2011) 1184-1193

Non-patent Literature 2: D Wei et al., “Automatic motion correction forin vivo human skin OCT angiography through combined rigid and nonrigidregistration,” J Biomed Opt, 22(6), 066013 (2017)

During such OCT measurement, however, a minute motion of a blood flow islikely to be contaminated with noise due to body motion, and it is thusnot easy to acquire a clear image of a network of blood vessels.Although various filters have been proposed for removal of such noise(refer to Non-patent Literature 2, for example), these aredisadvantageous in complicated arithmetic processing, which increasesthe cost.

SUMMARY OF INVENTION

In view of the above and other circumstances, one of objects of thepresent invention is to realize visualization of a network of bloodvessels of skin using the OCT with high accuracy by a simple technique.

An embodiment of the present invention relates to a blood vesselvisualizing device that includes an optical system using the OCT, andvisualizes a network of blood vessels of skin. The device includes: anoptical mechanism that guides light from a light source to tissue of theskin to scan the skin tissue; a control computation unit that controlsdriving of the optical mechanism, acquires tomographic images of theskin by processing optical interference signals from the optical system,and calculates a network of blood vessels on the basis of thetomographic images; and a display unit that displays an image of thenetwork of blood vessels. The control computation unit computesautocorrelation values at coordinates in epidermis corresponding regionsof a plurality of acquired topographic images of each skin site,excludes combinations of tomographic images with the computedautocorrelation values corresponding to predetermined lowautocorrelation, then computes autocorrelation values at coordinates indermis corresponding regions, and determines, as blood vessels or bloodvessel candidates, coordinates at which the autocorrelation values inthe dermis corresponding regions are within a predetermined lowcorrelation range, and calculates the network of blood vessels.

Another embodiment of the present invention relates to a blood vesselvisualizing method for visualizing a network of blood vessels of skin.The method includes: a tomographic image acquiring step of acquiringtomographic images of the skin by using optical coherence tomography; acomputation specifying step of determining tomographic images in whichinfluence of noise due to body motion exceeds a reference value amongthe acquired tomographic images, and excluding the determinedtomographic images from computation; a computing step of calculating anetwork of blood vessels on the basis of tomographic images subject tocomputation; and a displaying step of displaying the calculated networkof blood vessels.

The present invention achieves visualization of a network of bloodvessels of skin with high accuracy by a simple technique.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram schematically illustrating a configuration of ablood vessel visualizing device according to an example;

FIGS. 2A to 2C are explanatory views showing a basic method fordetecting a network of blood vessels;

FIGS. 3A to 3D are explanatory views showing the influence of noise indetection of a network of blood vessels by the OCT;

FIG. 4 is an explanatory diagram schematically illustrating a bloodvessel extracting method;

FIGS. 5A and 5B are explanatory diagrams schematically illustrating theblood vessel extracting method;

FIGS. 6A and 6B show results of calculation of a network of bloodvessels;

FIGS. 7A to 7D show results of calculation of a network of bloodvessels;

FIGS. 8A to 8C show a blood vessel thickness displaying process;

FIGS. 9A to 9C show results of visualization of blood vesselthicknesses;

FIGS. 10A and 10B show states of epidermis when thermal load is applied;

FIG. 11 is a flowchart illustrating a flow of a blood vessel networkvisualizing process;

FIG. 12 is a flowchart illustrating a body motion noise removing processin detail;

FIG. 13 is a flowchart illustrating a blood vessel extracting process indetail; and

FIG. 14 is a flowchart illustrating a blood vessel parameter displayingprocess in detail.

DETAILED DESCRIPTION

One embodiment of the present invention is a blood vessel visualizingdevice. The device includes an optical mechanism and a controlcomputation unit. The control computation unit controls driving of theoptical mechanism, and acquires tomographic images of skin by using theOCT. The control computation unit calculates the shape (two-dimensionalshape or three dimensional shape) of a network of blood vessels on thebasis of the tomographic images, and displays the mages on a displayunit. The device is capable of reducing body motion noise, extractingblood vessel sites with high accuracy, and visualizing the thicknessesof blood vessels in display processing of a network of blood vessels.

(1) Reduction of Body Motion Noise

In the present embodiment, an OCT image of each skin site from theepidermis to the dermis thereof is acquired a plurality of times (aplurality of times per one cross section). The control computation unitdetermines, among the acquired tomographic images, tomographic images inwhich the influence of body motion noise exceeds a reference value. Thecontrol computation unit then excludes the determined tomographic imagesfrom computation, and calculates a network of blood vessels on the basisof the remaining tomographic images. In this manner, an image of anetwork of blood vessels with reduced body motion noise can bedisplayed.

Specifically, before calculating a network of blood vessels on the basisof autocorrelation of tomographic images according to a technique of therelated art, a process of sorting out combinations of tomographic imagesto be used in the autocorrelation is performed. This is to narrow downthe computation to the combinations of tomographic images with littlebody motion noise by using the difference in the sensitivity of theepidermis to blood flows and to body motion. Specifically, no bloodvessels are present in the epidermis while blood vessels are present inthe dermis. Thus, the dermis has high sensitivity to the motion of bloodvessels (that is, blood flow) and likely to be displaced or deformed bythe blood flow. In contrast, because the epidermis is spaced from theblood vessels, the epidermis has low sensitivity to the blood flow, buthas high sensitivity to body motion. When the displacement of theepidermis is large, it can therefore be assumed that the displacement iscaused by body motion. On the basis of this idea, combinations oftomographic images in which the displacement of the epidermis is largerthan a criterion are removed from the computation of autocorrelation.

The control computation unit sets an epidermis corresponding region anda dermis corresponding region in a cross-sectional region of skin. Notethat an “epidermis corresponding region” may be part of an epidermisregion, and may be a region in which a displacement caused by a bloodflow is relatively small while a displacement caused by body motion isrelatively large. A “dermis corresponding region” may be part of adermis region, and may be a region in which a displacement caused by ablood flow is relatively large. More specifically, a reticular layer ofthe dermis belongs to a dermis corresponding region because the bloodvessels therein have relatively large diameters and a displacementthereof caused by a blood flow is large. A papillary layer of the dermisneed not be included in a dermis corresponding region because the bloodvessels therein have relatively small diameters and a displacementthereof caused by a blood flow is small. “Coordinates”, which may bespatial coordinates set in the OCT, define positions (cross-sectionalpositions) of pixels constituting a tomographic image.

The control computation unit may define an epidermis correspondingregion and a dermis corresponding region on the basis of an intensityprofile in the depth direction of an acquired tomographic image. An“intensity profile” may show OCT intensity (light intensity).

The control computation unit computes autocorrelation values atcoordinates in epidermis corresponding regions in a plurality ofacquired tomographic images of a skin site (first correlation acquiringprocess). Subsequently, combinations of tomographic images with thecomputed autocorrelation values corresponding to predetermined lowautocorrelation are excluded from subsequent computation (computationspecifying process). This computation specifying process (that is, theprocess of excluding combinations of tomographic images in which theepidermis corresponding regions have low autocorrelation) reducescontamination with body motion noise.

The control computation unit computes autocorrelation values ofcoordinates in dermis corresponding regions in the tomographic imagesremaining after the excluding process (second correlation acquiringprocess). The control computation unit then determines, as blood vesselsor blood vessel candidates, coordinates in the dermis correspondingregions having autocorrelation values present within a predetermined lowcorrelation range, to calculate a network of blood vessels.Specifically, the thus calculated coordinates (pixels corresponding tothe coordinates) may be determined as blood vessels, and a network ofblood vessels may be calculated as a connection of such coordinates.Alternatively, the thus calculated coordinates may just be determined asblood vessel candidates, and may further be determined as blood vesselsdepending on an additional condition. The “low correlation range” can beset to a proper range through experiments, analysis, or the like.

According to the present embodiment, visualization of a network of bloodvessels is achieved with high accuracy by a simple technique of removingtomographic images with much body motion noise from computation among aplurality of acquired tomographic images of each skin site.

In addition, a blood vessel visualization program using theabove-described technology may be built. The program causes a computerto implement a function of computing first autocorrelation values, whichare autocorrelation values in epidermis corresponding regions, of aplurality of tomographic images of each skin site acquired by the OCT, afunction of excluding combinations of tomographic images with the firstautocorrelation values corresponding to predetermined lowautocorrelation and then computing second autocorrelation values, whichare autocorrelation values in dermis corresponding regions, a functionof calculating a network of blood vessels on the basis of the secondautocorrelation values, and a function of outputting signals to displaythe calculated network of blood vessels. The program may be recorded ona computer-readable recording medium.

(2) Accurate Extraction of Blood Vessel Sites

For calculation of a network of blood vessels of skin by the OCT, ablood vessel may be determined on the basis of the OCT intensity (lightintensity, or intensity). This is based on blood vessels' property ofhaving lower OCT intensities than surrounding tissue. Sites with low OCTintensities may, however, include lymphatic vessels. In the presentembodiment, blood vessels and lymphatic vessels are distinguished fromeach other on the basis of a preset threshold of OCT intensity, whichenables extraction of blood vessel sites with high accuracy.

The control computation unit sets a reference profile obtained byfunction approximation of an intensity profile in the depth direction ofan acquired tomographic image. An “intensity profile” may show the OCTintensity. A tomographic image may be acquired once or may be acquired aplurality of times as described above. In the latter case, an average ofOCT intensities in a plurality of images may be obtained. For the“function approximation”, linear fitting such as the least-squaresmethod may be employed. For computation of a network of blood vesselsfocusing on dermis corresponding regions, the setting on the referenceprofile may be limited to the dermis corresponding regions.

Regarding an intensity value in the depth direction of a tomographicimage, the control computation unit calculates a difference between anintensity value on the reference profile and an actual intensity valueas an outlier (outlier level). The “intensity value on the referenceprofile” refers to the intensity (also referred to as “referenceintensity”) of surrounding tissue other than blood vessels and lymphaticvessels. The intensities of blood vessels and lymphatic vessels aresignificantly lower than the reference intensity. Thus, a site can bedetermined not to be surrounding tissue, that is, determined to be ablood vessel or a lymphatic vessel on the basis of calculation of themagnitude of the “outlier”, which is the difference between an actuallydetected intensity and the reference intensity.

The control computation unit determines coordinates having the outlierswithin a preset blood vessel determination range (a range from a firstthreshold to a second threshold) to be blood vessels or blood vesselcandidates, to calculate a network of blood vessels. Specifically, thethus calculated coordinates (pixels corresponding to the coordinates)may be determined as blood vessels, and a network of blood vessels maybe calculated as a connection of such coordinates. Alternatively, thethus calculated coordinates may just be determined as blood vesselcandidates, and may further be determined as blood vessels depending onan additional condition. The “blood vessel determination range” can beset to a proper range as a range substantially excluding lymphaticvessels and tissue surrounding blood vessels by experiments, analysis,or the like.

The control computation unit may determine coordinates having theoutliers within a lymphatic vessel determination range (a rangeexceeding the second threshold), which is set to a lower intensity rangethan the blood vessel determination range, to be lymphatic vessels orlymphatic vessel candidates. Specifically, the thus calculatedcoordinates (pixels corresponding to the coordinates) may be determinedas lymphatic vessels. Alternatively, the thus calculated coordinates mayjust be determined as lymphatic vessel candidates, and may further bedetermined as lymphatic vessels depending on an additional condition.The “lymphatic vessel determination range” can be set to a proper rangeas a range substantially excluding blood vessels and tissue surroundinglymphatic vessels by experiments, analysis, or the like. The calculatedlymphatic vessels and blood vessels may be displayed on a display unitin such a manner that the lymphatic vessels and the blood vessels aredistinguished from each other, such as in different colors or differentpatterns from each other. Both of the lymphatic vessels and the bloodvessels may be displayed, and the display may be switched to either ofthe lymphatic vessels and the blood vessels as appropriate.Alternatively, the lymphatic vessels may be displayed instead of theblood vessels, and the device of the present embodiment may function asa “lymphatic vessel visualizing device”.

Such a blood vessel extracting method may be applied to the process ofcomputing a network of blood vessels in addition to the body motionnoise removing method described above. Specifically, the OCT intensitiesof blood vessel candidates obtained after the body motion noise removaldescribed above may be calculated as “actual intensity values”. Anaverage of intensity values in the remaining tomographic images may beused as an “actual intensity value”. Blood vessel candidates havingintensity values within the blood vessel determination range may bedetermined to be blood vessels. Such an additional process of the bloodvessel extracting method may be performed only on dermis correspondingregions.

In addition, a blood vessel visualization program using theabove-described technology may be built. The program causes a computerto implement a function of setting a reference profile obtained byfunction approximation of an intensity profile in the depth direction ofa tomographic image acquired by the OCT, a function of calculating adifference between an intensity value on the reference profile and anactual intensity value as an outlier regarding the intensity value inthe depth direction of the tomographic image, determining coordinateshaving the outliers within the preset blood vessel determination rangeto be blood vessels or blood vessel candidates, and calculating anetwork of blood vessels, and a function of outputting signals todisplay the calculated network of blood vessels. The program may berecorded on a computer-readable recording medium.

(3) Visualization of Blood Vessel Thickness

In general, supply of nutrients and excretion of wastes are smoother andskin is considered to be healthier as the capillaries in the skin arethicker. Thus, evaluation of skin can be more appropriately achieved ifthe thicknesses of blood vessels are visualized more properly inaddition to visual display of a network of blood vessels as describedabove. Thus, in the present embodiment, the thicknesses of blood vesselsare visually displayed as one of parameters of a network of bloodvessels.

As described above, the control computation unit determines coordinateswith the autocorrelation values of a plurality of acquired tomographicimages of a skin site being within the preset low correlation range tobe blood vessels, to calculate a network of blood vessels.Alternatively, the control computation unit may determine coordinateswith OCT intensities equal to or smaller than a blood vesseldetermination reference value (or preferably coordinates within theblood vessel determination range) to be blood vessels, and calculate anetwork of blood vessels. In the present embodiment, a radius from eachset of blood vessel corresponding coordinates determined ascorresponding to a blood vessel and within which other sets of bloodvessel corresponding coordinates are present is defined as a bloodvessel radius. The “blood vessel radius” may be on the assumption of avirtual circle having its center at blood vessel correspondingcoordinates. Alternatively, a center of a polygon close to a circle orother shapes may be set, and the blood vessel radius may be definedusing the diameter of the polygon or the like (approximation to theradius). The control computation unit expresses the thicknesses of anetwork of blood vessels by superimposing distinctions based on theblood vessel radii from the respective sets of blood vesselcorresponding coordinates onto the image of the network of bloodvessels.

The control computation unit may gradually increase a radius from eachset of blood vessel corresponding coordinates. When the radius hasreached a value at which the proportion of surrounding blood vesselcoordinates becomes lower than a preset proportion determinationreference value, the control computation unit may determine, as theblood vessel radius, this value or a value immediately before theproportion becomes lower than the preset proportion determinationreference value. Alternatively, in view of an error range, the controlcomputation unit may determine, as the blood vessel radius, a value ofthe radius before or after the proportion becomes lower than the presetproportion determination reference value. The “proportion determinationreference value” may be appropriately set depending on the resolution ofthe images, such as 98% or higher, or more preferably 99% or higher,which is substantially 100% (including the error range). The controlcomputation unit may alternatively determine, as the “blood vesselradius”, a value of the radius when or immediately before the proportionstarts to decrease such as from 100% to 99%.

Specifically, the control computation unit may express the thicknessesof a network of blood vessels by using different colors at respectivesets of blood vessel corresponding coordinates depending on themagnitudes of the blood vessel radii. The distinctions based on themagnitudes of the blood vessel radii are made in a superimposing manneras described above, which makes the thicknesses of blood vessels clearat a glance, as will also be presented in an example described later. Inparticular, this improves the visibility of blood vessel shapes at partshaving complicated shapes such as diverging points and converging pointsof blood vessels.

In addition, a blood vessel visualization program using theabove-described technology may be built. The program causes a computerto implement a function computing a network of blood vessels of skin onthe basis of a tomographic image acquired by the OCT, and a function ofdetermining, as a blood vessel radius, a radius from each set of bloodvessel corresponding coordinates determined as corresponding to a bloodvessel in the calculation of the network of blood vessels and withinwhich other sets of blood vessel corresponding coordinates are present,and outputting signals to make distinctions based on the magnitudes ofthe blood vessel radii from the respective sets of blood vesselcorresponding coordinates in a superimposing manner with display of thenetwork of blood vessels. The program may be recorded on acomputer-readable recording medium.

An example according to the present embodiment will now be described indetail with reference to the drawings.

EXAMPLE

FIG. 1 is a diagram schematically illustrating a configuration of ablood vessel visualizing device according to the example. The bloodvessel visualizing device tomographically measures skin tissue inmicroscale, and visualizes the capillaries in the skin. The OCT is usedfor the tomographic measurement.

As illustrated in FIG. 1, an OCT device 1 includes a light source 2, anobject arm 4, a reference arm 6, optical mechanisms 8 and 10, an opticaldetection device 12, a control computation unit 14, and a display device16. The respective optical components are connected with each other byoptical fibers. While an optical system based on a Mach-Zehnderinterferometer is presented in FIG. 1, other optical systems such as aMichelson interferometer may alternatively be used.

While swept source OCT (SS-OCT) is used in this example, other types ofOCT such as time domain OCT (TD-OCT) or spectral domain OCT (SD-OCT) maybe used instead. The SS-OCT enables acquisition of data with highmeasurement sensitivity by using a light source with temporally sweptemission wavelength without mechanical sweeping along the referenceoptical path, which is preferable in that high temporal resolution andhigh position detecting accuracy are achieved.

Light emitted from the light source 2 is split by a coupler 18 (beamsplitter). One beam from the coupler 18 is guided to the object arm 4and becomes object light, and the other is guided to the reference arm 6and becomes reference light. The object light in the object arm 4 isguided to the optical mechanism 8 via a circulator 20, and directed toan object to be measured (hereinafter referred to as an “object S”). Theobject S is skin in this example. The object light is reflected asbackscattered light at the surface and a cross section of the object S,returned to the circulator 20, and then guided to a coupler 22.

Meanwhile, the reference light at the reference arm 6 is guided to theoptical mechanism 10 via a circulator 24. The reference light isreflected by a reference mirror 26 of the optical mechanism 10, returnedto the circulator 24, and then guided to the coupler 22. Thus, theobject light and the reference light are combined (superimposed) by thecoupler 22, and interference light of the object light and the referencelight is detected by the optical detection device 12. The opticaldetection device 12 detects the interference light as an opticalinterference signal (a signal indicating interference light intensity).The optical interference signal is input to the control computation unit14 via an AD converter 28.

The control computation unit 14 performs control of the entire opticalsystem, and arithmetic processing for outputting images using the OCT.Command signals from the control computation unit 14 are input to thelight source 2, the optical mechanisms 8 and 10, and the like via a DAconverter, which is not illustrated. The control computation unit 14processes the optical interference signal input to the optical detectiondevice 12, and acquires a tomographic image of the object S using theOCT. The control computation unit 14 then computes tomographicdistribution of a network of blood vessels in the object S on the basisof the tomographic image data by a technique described later.

This will be described in more detail below.

The light source 2 is a wavelength swept light source that emits lightwith temporally swept emission wavelengths. Light emitted from the lightsource 2 is split by the coupler 18 into the object light and thereference light, which are guided to the object arm 4 and the referencearm 6, respectively.

The optical mechanism 8 is included in the object arm 4, and includes amechanism for guiding light from the light source 2 to the object S toscan the object S, and a drive unit for driving the mechanism. Theoptical mechanism 8 includes a collimator lens 30, a two-axisgalvanometer mirror 32, and an object lens 34. The object lens 34 isarranged to face the object S. Light having passed through thecirculator 20 is guided to the galvanometer mirror 32 via the collimatorlens 30, scanned in the x-axis direction and the y-axis direction, anddirected to the object S. Light reflected by the object S is returned asobject light to the circulator 20, and guided to the coupler 22.

The optical mechanism 10 is included in the reference arm 6, andincludes a collimator lens 40, a focusing lens 27, and the referencemirror 26. Light having passed through the circulator 24 is focused onthe reference mirror 26 by the focusing lens 27 via the collimator lens40. This reference light is reflected by the reference mirror 26, thusreturned to the circulator 24, and guided to the coupler 22. Thereference light is then superimposed with the object light, and sent asinterference light to the optical detection device 12.

The optical detection device 12 includes a photodetector 42 and anamplifier 44. The interference light obtained through the coupler 22 isdetected as an optical interference signal by the photodetector 42. Theoptical interference signal is input to the control computation unit 14via the amplifier 44 and the AD converter 28.

The control computation unit 14 includes a CPU, a ROM, a RAM, a harddisk, and the like. The control computation unit 14 performs, by thehardware and software, control of the entire optical system, andarithmetic processing for outputting images by the OCT. The controlcomputation unit 14 controls driving of the optical mechanisms 8 and 10,processes the optical interference signal output from the opticaldetection device 12 on the basis of the driving, and acquirestomographic images of the object S obtained by the OCT. The controlcomputation unit 14 then computes a network of blood vessels in theobject S by a technique that will be described later on the basis of thetomographic image data.

The display device 16 is constituted by a liquid crystal display, forexample, and functions as a “display unit”. The display device 16displays the network of blood vessels in the object S computed by thecontrol computation unit 14 in two-dimensional or three-dimensionalvisualization.

A method of arithmetic processing for calculating a network of bloodvessels in skin will now be explained.

As described above, according to the OCT, the object light (reflectedlight from the object S) having passed through the object arm 4 and thereference light having passed through the reference arm 6 are combined,and detected as an optical interference signal by the optical detectiondevice 12. The control computation unit 14 is capable of acquiring theoptical interference signal as a tomographic image of the object S basedon the interference light intensity (OCT intensity).

A coherence length l_(c), which represents the resolution in the opticalaxis direction (depth direction) of the OCT is determined by anautocorrelation function of the light source. Herein, the coherencelength l_(c) is the half width at half maximum of the envelope of theautocorrelation function, and can be expressed by the followingexpression (1).

$\begin{matrix}{l_{c} = {\frac{2\mspace{14mu}\ln\mspace{14mu} 2}{\pi}\left( \frac{\lambda_{c}^{2}}{\Delta\lambda} \right)}} & (1)\end{matrix}$In the expression (1), λ_(c) represents the center wavelength of a beam,and Δλ represents the full width at half maximum of the beam.

In addition, the resolution in the direction perpendicular to theoptical axis (beam scanning direction) is ½ of a beam-spot diameter D onthe basis of the focusing performance of a focusing lens. The beam-spotdiameter ΔΩ can be expressed by the following expression (2).

$\begin{matrix}{{\Delta\Omega} = {\frac{4\lambda_{c}}{\pi}\left( \frac{f}{d} \right)}} & (2)\end{matrix}$In the expression (2), d represents the diameter of a beam incident onthe focusing lens, and f represents the focal point of the focusinglens.

A network of blood vessels (shapes of blood vessels and changes thereof)can be calculated through computation of autocorrelation of a pluralityof tomographic images of each site acquired by the OCT. FIGS. 2A to 2Care explanatory views showing a basic method for detecting a network ofblood vessels. FIG. 2A shows a method of measuring skin (an object S) bythe OCT. FIG. 2B illustrates a two-dimensional tomographic image, andFIG. 2C illustrates a three-dimensional tomographic image.

As illustrated in FIG. 2A, capillaries run in and under dermis of skin.In FIG. 2A, arteries are illustrated by solid lines, and veins areillustrated by alternate long and short dashed lines. No blood vesselsare present in epidermis. Portions projecting toward the epidermis inupper part of the dermis correspond to a papillary layer, which includesvery thin blood vessels. A reticular layer is present under thepapillary layer. Relatively thick blood vessels are present in thereticular layer.

In OCT measurement, the optical axis direction of the object light isset to the depth direction of the skin, and referred to as a Zdirection. An X direction and a Y direction are set to directionsperpendicular to the Z direction. First, a two-dimensional measurementregion is set on a Z-X plane (see a frame in broken line), andZ-direction scanning using wavelength sweeping according to the SS-OCTis performed. The Z-direction scanning is repeated while being shiftedin the X direction. As a result, a two-dimensional tomographic image asshown in FIG. 2B is obtained. Y-direction scanning is further performed,and a three-dimensional tomographic image as shown in FIG. 2C is thusobtained. Because such OCT measurement has high resolution inmicroscale, even noise caused by body motion of a subject (body motionnoise) is not negligible in some cases.

FIGS. 3A to 3D are explanatory views showing the influence of noise indetection of a network of blood vessels by the OCT. FIG. 3A shows theinfluence of body motion noise. FIGS. 3B to 3D show the influence ofnoise due to blood flows. In FIG. 3A, high-intensity portions (whiteportions) represent a network of blood vessels at a predeterminedcross-section (X-Y cross-section). It can be seen that body motion noiseis superimposed on the network of blood vessels, which makes the entireimage blurred in white.

In addition, as shown in FIG. 3B, in three-dimensional display, it canbe seen that lower part of a Z-X cross section is blurred in white. Thisis considered to be because blood flows in the reticular layer of thedermis have increased, resulting in that a detected image has beenshaken in the Z direction, which has caused a so-called “motion ghost G”shown in FIG. 3C. When the motion ghost G occurs, calculated bloodvessel likelihood data extends in the Z direction as shown in FIG. 3D,which does not correspond to the actual blood vessels. Noise caused bythe motion ghost will also be referred to as “blood flow noise”. In thisexample, a body motion noise removing process and a blood vesselextracting process are performed to solve such problems.

Body Motion Noise Removing Process

In calculation of a network of blood vessels, an interrogation region(inspection region) (a region of 3×3 pixels, for example) is set in eachof continuously acquired tomographic images of a skin site, andautocorrelation within the interrogation regions is computed.Specifically, the identity (similarity) of images at the sanecoordinates in the interrogation regions is calculated as anautocorrelation value. As the autocorrelation is higher, it isdetermined that less displacement (deformation) is present in theimages, that is, the motion of skin tissue at the coordinates issmaller. Conversely, as the autocorrelation is lower, it is determinedthat the motion of skin tissue at the coordinates is larger. Becauseblood vessels vary depending on blood flows, blood vessels have lowerautocorrelation than surrounding tissue. On the basis of this feature,coordinates (pixel) at which autocorrelation is low can be determined ashaving high blood vessel likelihood, that is, as a blood vessel or ablood vessel candidate.

When body motion noise is present, however, tissue also varies(deforms), and has low autocorrelation. Thus, when the autocorrelationis low, it is preferable that it can be determined whether the lowautocorrelation is due to blood flows or body motion noise, and that thelatter can be removed. Focus is therefore placed on a structuraldifference in that no blood vessels are present in epidermis while bloodvessels are present in dermis. Because the epidermis does not includeblood vessels, the autocorrelation in the epidermis should normally behigh. When the autocorrelation is nonetheless significantly low in theepidermis, it can be likely to be due to the influence of body motionnoise.

Typically, in obtaining autocorrelation in a plurality of tomographicimages acquired for calculation of a network of blood vessels,autocorrelation values are calculated for all combinations of thetomographic images, and the autocorrelation is evaluated on the basis ofan average of the calculated autocorrelation values. In this example,combinations of topographic images in which the autocorrelation in theepidermis is low are excluded in advance, so that contamination of bodymotion noise is prevented or reduced. Autocorrelation is then obtainedfrom the remaining combinations of the tomographic images, and a networkof blood vessels with reduced body motion noise is calculated.

Specifically, the following arithmetic processing is performed.

When a tomographic image of one cross section is acquired T times by theOCT, the OCT intensity (light intensity) at coordinates (p,q) in a t-thtomographic image are represented by I_(t)(p,q), and an average of theOCT intensities within an interrogation region with its center at thecoordinates (p,q) is represented by ⁻I_(t)(p,q). In this case, anautocorrelation value P_(epi) ^(t1,t2) (p,q) obtained by normalizationat the coordinates (p,q) in an epidermis corresponding regions in t1-thand t2-th (t1≠t2) tomographic images is expressed by the followingexpression (3).

$\begin{matrix}{{P_{epi}^{{t\; 1},{t\; 2}}\left( {p,q} \right)} = \frac{\Sigma_{q}{\Sigma_{p}\left( {{I_{t\; 1}\left( {p,q} \right)} - \overset{\_}{I_{t\; 1}}} \right)}\left( {{I_{t\; 2}\left( {p,q} \right)} - \overset{\_}{I_{t\; 2}}} \right)}{\sqrt{\Sigma_{q}{\Sigma_{p}\left( {{I_{t\; 1}\left( {p,q} \right)} - \overset{\_}{I_{t\; 1}}} \right)}^{2} \times \Sigma_{q}{\Sigma_{p}\left( {{I_{t\; 2}\left( {p,q} \right)} - \overset{\_}{I_{t\; 2}}} \right)}^{2}}}} & (3)\end{matrix}$

The control computation unit 14 obtains a set PS_(epi), which isrearrangement in descending order of a set P_(epi) of suchautocorrelation values P_(epi) ^(t1,t2)(p,q) (a set of autocorrelationdata), by the following expression (4).P _(epi)

{P _(epi) ^(1,2) ,P _(epi) ^(1,3) , . . . ,P _(epi) ^(T−1,T})PS _(epi)=sort{P _(epi) ,ASC}  (4)

The control computation unit 14 then deletes (M+1)-th and subsequentdata in the set PS_(epi), M being a predetermined number. That is, datawith which predetermined low autocorrelation is obtained in an epidermiscorresponding region are determined as being affected by body motionnoise and excluded. In other words, combinations of tomographic imageshaving low autocorrelation in an epidermis corresponding region areexcluded. The remaining data, that is, the first to M-th data with highautocorrelation in the epidermis corresponding region are used fordetermination of a network of blood vessels.

Specifically, a normalized autocorrelation value P_(derm) ^(t1,t2)(p,q)in a dermis corresponding region is calculated for the remainingcombinations of tomographic images by the following expression (5).

$\begin{matrix}{{P_{derm}^{{t\; 1},{t\; 2}}\left( {p,q} \right)} = \frac{\Sigma_{q}{\Sigma_{p}\left( {{I_{t\; 1}\left( {p,q} \right)} - \overset{\_}{I_{t\; 1}}} \right)}\left( {{I_{t\; 2}\left( {p,q} \right)} - \overset{\_}{I_{t\; 2}}} \right)}{\sqrt{\Sigma_{q}{\Sigma_{p}\left( {{I_{t\; 1}\left( {p,q} \right)} - \overset{\_}{I_{t\; 1}}} \right)}^{2} \times \Sigma_{q}{\Sigma_{p}\left( {{I_{t\; 2}\left( {p,q} \right)} - \overset{\_}{I_{t\; 2}}} \right)}^{2}}}} & (5)\end{matrix}$The combinations of t1 and t2 are those that are not deleted in theaforementioned expression (4).

The control computation unit 14 then determines coordinates at which theautocorrelation value P_(derm) ^(t1,t2)(p,q) is in a predetermined lowcorrelation range to be blood vessel candidates.

Blood Vessel Extracting Process

Some of the blood vessel candidates obtained as described above mayinclude lymphatic vessels. In addition, a motion ghost may be present ata portion of a large blood flow. To solve such problems, a process forselecting blood vessels from blood vessel candidates is performed.

FIG. 4, FIGS. 5A and 5B are explanatory diagrams schematicallyillustrating a blood vessel extracting method.

In this example, as illustrated in the right part of FIG. 4, anintensity profile showing the relation between the depth from a skinsurface and OCT intensity is obtained on the basis of the acquiredtomographic images. Herein, the depth is expressed by a pixel value inthe Z direction. The OCT intensity may be an average of data in aplurality of tomographic images of a skin site. A position with thehighest OCT intensity (depth=0) corresponds to the skin surface. Theintensity profile is a curve including a minimum value and a maximumvalue as illustrated. A range from the skin surface to approximately theminimum value is considered as corresponding to the epidermis. Inaddition, according to a literature (Neerken, S., Characterization ofage-related effects in human skin, J Biomed Opt, 9 (2004) 274-281.), theboundary between a papillary layer corresponding part and a reticularlayer corresponding part in the dermis is considered as being presentbetween the minimum value and the maximum value.

Note that a portion of a large blood flow at which a motion ghost mayoccur is considered to correspond to a reticular layer correspondingpart in which blood vessels are relatively thick. Thus, blood vesselcandidates having high blood vessel likelihood in the reticular layercorresponding part are extracted.

FIG. 5A shows a reference profile, and FIG. 5B shows a blood vesseldetermination range.

The control computation unit 14 holds the reference profile obtained byfunction approximation of the intensity profile illustrated in the rightpart of FIG. 4 (FIG. 5A). More specifically, the reference profile isobtained by linear fitting of the intensity profile at a depth deeperthan the position at which the OCT intensity is maximum in the reticularlayer corresponding part (see a frame in alternate long and short dashedline). Such function approximation makes the OCT intensity on thereference profile closer to the intensity of skin tissue surroundingblood vessels and lymphatic vessels. In other words, when the OCTintensity at a position deviates significantly from the referenceprofile, the position is considered as corresponding to a blood vesselor a lymphatic vessel.

Thus, in this example, as illustrated in FIG. 5B, a difference betweenthe intensity value on the reference profile and the actual intensityvalue at the same depth is defined as an outlier (outlier level). Whilean “outlier V” is defined as a value obtained by subtracting theintensity value on the reference profile from the actual intensity valueand is thus a negative value in the example illustrated in FIG. 5B, theoutlier V may be defined as a value obtained by the reverse of thesubtraction. A range in which the outlier V is V1 to V2 is the “bloodvessel determination range”, and a range in which the outlier is largerthan V2 is the “lymphatic vessel determination range”.

Coordinates (pixel) having the outlier within the blood vesseldetermination range in an OCT image can be determined to correspond to ablood vessel or a blood vessel candidate. Among blood vessel candidatesin the reticular layer corresponding part obtained through the bodymotion noise removing process, the control computation unit 14determines a blood vessel candidate having the outlier within the bloodvessel determination range to be a blood vessel.

FIGS. 6A, 6B, 7A to 7D show results of calculation of a network of bloodvessels. FIG. 6A shows a case where the body motion noise removingprocess has not been performed, and FIG. 6B shows a case where the bodymotion noise removing process has been performed. FIGS. 7A to 7D show aplurality of images (images of X-Y cross sections) at different depthsfrom the skin surface. The depths are 63 μm, 108 μm, 323 μm, and 431 μm,respectively. In each of FIGS. 7A to 7D, the upper part shows an OCTimage, and the lower part shows a result of calculation of a network ofblood vessels.

In FIGS. 6A and 6B, it can be seen that the body motion noise removingprocess as in this example clearly reduces noise and increases thevisibility. In addition, in FIGS. 7A to 7D, it can be seen that anetwork of blood vessels is thicker as the depth under the skin islarger, and thus the obtained image is close to the actual bloodvessels.

Blood Vessel Parameter Displaying Process

In this example, information on the thicknesses of blood vessels isvisually displayed in a superimposing manner on the network of bloodvessels obtained as described above. FIGS. 8A to 8C illustrate processstages of a blood vessel thickness displaying process.

The control computation unit 14 calculates a blood vessel radius fromeach set of coordinates (blood vessel corresponding coordinates)determined to correspond to a blood vessel as described above. Asschematically illustrated in FIG. 8A, a radius from blood vesselcorresponding coordinates included in a network of blood vessels 50 andwithin which other sets of blood vessel corresponding coordinates arepresent is defined as a blood vessel radius r. Specifically, a radius ofa circle with its center at the set of blood vessel correspondingcoordinates is gradually increased, and the value of the radius when theratio of the area of the blood vessel corresponding coordinates to thearea of the circle starts to decrease is determined as the blood vesselradius r. Conceptually, the value of the radius of the circle with itscenter at the set of blood vessel corresponding coordinates when thecircle reaches the blood vessel wall corresponds to the blood vesselradius r. In the example illustrated in FIG. 8A, r1 is obtained as theblood vessel radius at coordinates p1, r2 is obtained as the bloodvessel radius at coordinates p2, and r3 is obtained as the blood vesselradius at coordinates p3.

In addition, as illustrated in FIG. 8B, the control computation unit 14uses different colors at respective sets of blood vessel correspondingcoordinates depending on the magnitudes of the blood vessel radii. Theblood vessel radius is smaller as the set of blood vessel correspondingcoordinates is closer to the blood vessel. To correct this, asillustrated in FIG. 8C, the control computation unit 14 subsequentlyapplies a circular Kernel maximum filter, which is a filter for fillinga circular region with a value of a center of the maximum circle withinthe region, to color the respective sets of blood correspondingcoordinates with different colors depending on the blood vessel radii.Although the blood vessel radii are equal and the blood vesselcorresponding coordinates are filled with the same color in the exampleillustrated in FIG 8C, a color change will be displayed at blood vesselcorresponding coordinates where the blood vessel radius changes.

FIGS. 9A to 9C show results of visualization of blood vesselthicknesses. Specifically, FIGS. 9A to 9C show processes of display ofthe blood vessel thicknesses. FIGS. 10A and 10B show states of theepidermis when thermal load is applied. FIG. 10A shows a state beforethermal load is applied, and FIG. 10B shows a state after thermal loadis applied.

The control computation unit 14 displays an image of a network of bloodvessels shown in FIG. 9A, and computes a thickness distinguishing imageshown in FIG. 9B. In addition, the control computation unit 14superimposes the thickness distinguishing image on the blood vesselnetwork image as shown in FIG. 9C to express the thicknesses of thenetwork of blood vessels.

As shown in FIGS. 10A and 10B, the thickness distinguishing imagechanges before and after application of thermal load. It can be seenthat the thicknesses are increased after the application of thermalload. Thus, a result that matches the actual state in which bloodvessels expand owing to thermal load is obtained.

Next, a flow of specific processes performed by the control computationunit 14 will be explained.

FIG. 11 is a flowchart illustrating a flow of a blood vessel networkvisualizing process performed by the control computation unit 14. Thisprocess is repeated for a predetermined computation period. The controlcomputation unit 14 acquires a plurality of optical interference signalsby the OCT while controlling driving of the light source 2 and theoptical mechanisms 8 and 10 (S10). The control computation unit 14sequentially performs the body motion noise removing process (S12), theblood vessel extracting process (S14), and the blood vessel parameterdisplaying process (S16) described above on the acquired OCT images.

FIG. 12 is a flowchart illustrating the body motion noise removingprocess in S12 of FIG. 11 in detail. The control computation unit 14calculates the intensity profile described above on the basis of theacquired OCT images (S20), and detect an epidermis corresponding region(S22). The control computation unit 14 then performs an autocorrelationprocess on the epidermis corresponding region (S24), and deletes imageswith low correlation to remove body motion noise (S26).

FIG. 13 is a flowchart illustrating the blood vessel extracting processin S14 of FIG. 11 in detail.

The control computation unit 14 acquires an autocorrelation value forthe remaining tomographic images that have not been removed in S26(S30), and determines coordinates present in the low correlation rangeas blood vessel candidates (S32).

In addition, the control computation unit 14 performs linear fitting onthe intensity profile, and sets a reference profile for a reticularlayer corresponding region (S34). The control computation unit 14 thencalculates an outlier of an intensity value (OCT intensity) for each ofcoordinates determined to be blood vessel candidates in S32 (S36), andcompares each of the outliers with the blood vessel determination range(S38). Subsequently, the control computation unit 14 determines bloodvessel candidates with the outliers being within the blood vesseldetermination range as blood vessels, and calculates a network of bloodvessels that is a set of the blood vessels (S40). The controlcomputation unit 14 then displays the thus obtained network of bloodvessels on the display device 16 (S42).

FIG. 14 is a flowchart illustrating the blood vessel parameterdisplaying process in S16 of FIG. 11 in detail. The control computationunit 14 calculates blood vessel radii from blood vessel correspondingcoordinates of the blood vessels obtained in S40 (S50), and computes athickness distinguishing image on the basis of the blood vessel radii(S52). The control computation unit 14 then displays the thicknessdistinguishing image superimposed on the blood vessel network image ofS42 (S54).

The description of the present invention given above is based onillustrative examples. It will be obvious to those skilled in the artthat the present invention is not limited to the particular examples butvarious modifications could be further developed within the technicalidea underlying the present invention.

In the above-described example, an example in which a plurality oftwo-dimensional tomographic images are acquired by the OCT (so-calledB-scan) and body motion noise is removed in units of B-scan on the basisof autocorrelation of the two-dimensional tomographic images has beenpresented. In a modification, autocorrelation may be obtained in unitsof Z-direction scanning (so-called A-scan), and body motion noise may beremoved in units of A-scan.

While an example in which autocorrelation of tomographic images iscomputed using two-dimensional coordinates has been presented in theabove-described example, the autocorrelation may be computed usingthree-dimensional coordinates.

In the above-described example, an example in which combinations oftomographic images with low autocorrelation in the epidermiscorresponding region are removed for removal of body motion noise hasbeen presented. In a modification, one tomographic image of suchcombination of tomographic images with which low autocorrelation isobtained may be deleted. The one tomographic image that is deleted is atomographic image with which low autocorrelation is likely to beobtained. For example, a tomographic image that is shared by a pluralityof combinations of tomographic images with which low autocorrelation isobtained may be deleted. In another modification, combinations oftomographic images with which low autocorrelation is obtained in all ofthe layers in a region to be analyzed including an epidermiscorresponding region may be deleted.

While the process of FIG. 8C is performed after the process of FIG. 8Bfor visualization of blood vessel thicknesses in the above-describedexample, the process of FIG. 8C may be omitted. The process of FIG. 8C,however, makes distinctions of the blood vessel thicknesses clearer.

Although not mentioned in the above-described example, a noise reducingprocess using a spatial frequency filter, a median filter or the likemay be performed to remove so-called line noise and salt-and-peppernoise after the body motion noise removing process or the blood vesselextracting process.

The present invention is not limited to the above-described examples andmodifications only, and the components may be further modified to arriveat various other examples without departing from the scope of theinvention. Various other examples may be further achieved by combining,as appropriate, a plurality of components disclosed in theabove-described example and modifications. Furthermore, one or some ofall of the components exemplified in the above-described example andmodifications may be left unused or removed.

What is claimed is:
 1. A blood vessel visualizing device that includesan optical system using optical coherence tomography, and visualizes anetwork of blood vessels of skin, the blood vessel visualizing devicecomprising: an optical mechanism that guides light from a light sourceto tissue of the skin to scan the skin tissue; a control computationunit that controls driving of the optical mechanism, acquirestomographic images of the skin by processing optical interferencesignals from the optical system, and calculates a network of bloodvessels on the basis of the tomographic images; and a display unit thatdisplays an image of the network of blood vessels, wherein the controlcomputation unit computes autocorrelation values at coordinates inepidermis corresponding regions of a plurality of acquired tomographicimages of each skin site, the control computation unit excludescombinations of tomographic images with the computed autocorrelationvalues corresponding to predetermined low autocorrelation, and thencomputes autocorrelation values at coordinates in dermis correspondingregions, and the control computation unit determines, as blood vesselsor blood vessel candidates, coordinates at which the autocorrelationvalues in the dermis corresponding regions are within a predeterminedlow correlation range, and calculates the network of blood vessels. 2.The blood vessel visualizing device according to claim 1, wherein thecontrol computation unit determines a region to be the epidermiscorresponding region or the dermis corresponding region on the basis ofan intensity profile in a depth direction in the acquired tomographicimages.
 3. The blood vessel visualizing device according to claim 2,wherein the control computation unit determines, as blood vesselcandidates, coordinates within the low correlation range, the controlcomputation unit holds a reference profile obtained by functionapproximation of an intensity profile in a depth direction in theacquired tomographic images, the control computation unit calculates adifference between an intensity value on the reference profile and anactual intensity value as an outlier, the intensity values being thosein a depth direction in the tomographic images, and determines, as ablood vessel candidate region, a region of coordinates with the outlierswithin a predetermined blood vessel determination range, and the controlcomputation unit determines blood vessel candidates within the bloodvessel candidate region to be blood vessels.
 4. The blood vesselvisualizing device according to claim 2, wherein the control computationunit obtains, as a blood vessel radius, a radius from a set of bloodvessel corresponding coordinates determined to be a blood vessel andwithin which other sets of blood vessel corresponding coordinates arepresent, and the control computation unit superimposes distinctionsbased on magnitudes of blood vessel radii from respective sets of bloodvessel corresponding coordinates on an image of the network of bloodvessels to express thicknesses of the network of blood vessels.
 5. Theblood vessel visualizing device according to claim 1, wherein thecontrol computation unit determines, as blood vessel candidates,coordinates within the low correlation range, the control computationunit holds a reference profile obtained by function approximation of anintensity profile in a depth direction in the acquired tomographicimages, the control computation unit calculates a difference between anintensity value on the reference profile and an actual intensity valueas an outlier, the intensity values being those in a depth direction inthe tomographic images, and determines, as a blood vessel candidateregion, a region of coordinates with the outliers within a predeterminedblood vessel determination range, and the control computation unitdetermines blood vessel candidates within the blood vessel candidateregion to be blood vessels.
 6. The blood vessel visualizing deviceaccording to claim 5, wherein the control computation unit obtains, as ablood vessel radius, a radius from a set of blood vessel correspondingcoordinates determined to be a blood vessel and within which other setsof blood vessel corresponding coordinates are present, and the controlcomputation unit superimposes distinctions based on magnitudes of bloodvessel radii from respective sets of blood vessel correspondingcoordinates on an image of the network of blood vessels to expressthicknesses of the network of blood vessels.
 7. The blood vesselvisualizing device according to claim 1, wherein the control computationunit obtains, as a blood vessel radius, a radius from a set of bloodvessel corresponding coordinates determined to be a blood vessel andwithin which other sets of blood vessel corresponding coordinates arepresent, and the control computation unit superimposes distinctionsbased on magnitudes of blood vessel radii from respective sets of bloodvessel corresponding coordinates on an image of the network of bloodvessels to express thicknesses of the network of blood vessels.
 8. Ablood vessel visualizing method for visualizing a network of bloodvessels of skin, the method comprising: a tomographic image acquiringstep of acquiring a plurality of tomographic images of a skin site byusing optical coherence tomography; a first correlation acquiring stepof acquiring autocorrelation in epidermis corresponding regions of theacquired tomographic images; a second correlation acquiring step ofexcluding combinations of tomographic images with the acquiredautocorrelation corresponding to predetermined low autocorrelation, andthen acquiring autocorrelation in dermis corresponding regions; acomputing step of calculating a network of blood vessels on the basis ofthe autocorrelation in the dermis corresponding regions; and adisplaying step of displaying the calculated network of blood vessels.9. A blood vessel visualizing method for visualizing a network of bloodvessels of skin, the method comprising: a tomographic image acquiringstep of acquiring tomographic images of the skin by using opticalcoherence tomography; a computation specifying step of determiningtomographic images in which influence of noise due to body motionexceeds a reference value among the acquired tomographic images, andexcluding the determined tomographic images from computation; acomputing step of calculating a network of blood vessels on the basis oftomographic images subject to computation; and a displaying step ofdisplaying the calculated network of blood vessels.
 10. A non-transitorycomputer readable medium comprising a program for causing a computer toimplement: a function of computing first autocorrelation values of aplurality of tomographic images of a skin site acquired by using opticalcoherence tomography, a first autocorrelation value being anautocorrelation value in epidermis corresponding regions; a function ofexcluding combinations of tomographic images with the firstautocorrelation values corresponding to predetermined lowautocorrelation, and then computing second autocorrelation values, thesecond autocorrelation values being autocorrelation values in dermiscorresponding regions; a function of calculating a network of bloodvessels on the basis of the second autocorrelation values; and afunction of outputting signals to display the calculated network ofblood vessels.